Machine Learning insights for behavioural data analysis supporting the Autonomous Vehicles scenario

The advent of the digital innovation era is changing service, use, and resources management paradigms, offering a wide range of new and essential opportunities. In particular, the advent of the Internet of Things (IoT), i.e. the ability to connect individual objects to the Internet, also capable of communicating autonomously, has its particular declination on the connected vehicle. It is combined with the potential of advanced sensors placed pervasively on vehicles, which offer multi-functional monitoring capabilities of the entire system: from individual components up to the whole vehicle, including driver behaviour and conditions and many exogenous parameters to the vehicle (road and weather conditions, congestion, risk situations, changes to mobility plans, etc.). In this perspective, Machine Learning (ML) models can transform raw data into new knowledge; they can contribute in an innovative way to define and suggest decisions, strategies, and criteria for resource use. Nowadays, most intelligent mobility projects also integrate artificial intelligence (AI) and ML solutions. In this paper, we present and discuss the application of unsupervised learning techniques on a Vehicular IoT dataset. The main goal is to generate new knowledge about a geographical zone by analyzing historical drivers behavioural data. The autonomous vehicle’s framework can exploit the generated valuable insights to optimize the routes and prevent critical issues. IEEE